Biomarker classification derived from finite growth mixture modeling with a time-varying covariate: an example with phosphorus and glomerular filtration rate

DOI10.1080/02664763.2014.957263OpenAlexW2066545256MaRDI QIDQ5130156

Sterling McPherson, Celestina Barbosa-Leiker

Publication date: 4 November 2020

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664763.2014.957263

zbMATH Keywords

time-varying covariatechronic kidney diseasebiomarker classificationfinite growth mixture modelinglatent growth curve modeling

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• Mplus

Cites Work

• Unnamed Item
• On structural equation modeling with data that are not missing completely at random
• Some contributions to maximum likelihood factor analysis
• A note on 'Testing the number of components in a normal mixture'
• Latent class and finite mixture models for multilevel data sets
• Finite mixture models
• Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm
• An example of a two-part latent growth curve model for semicontinuous outcomes in the health sciences
• General growth mixture modeling for randomized preventive interventions

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